Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations

Bhattarai, Bishnu P.; Myers, Kurt S.; Bak-Jensen, Brigitte; de Cerio Mendaza, Iker Diaz; Turk, Robert J.; Gentle, Jake P.

doi:10.1016/j.ijepes.2017.05.005

Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations

Journal Article · Wed May 17 00:00:00 EDT 2017 · International Journal of Electrical Power and Energy Systems

DOI:https://doi.org/10.1016/j.ijepes.2017.05.005· OSTI ID:1375244

Bhattarai, Bishnu P. ^[1]; Myers, Kurt S. ^[1]; Bak-Jensen, Brigitte ^[2]; de Cerio Mendaza, Iker Diaz ^[3]; Turk, Robert J. ^[1]; Gentle, Jake P. ^[1]

Idaho National Lab. (INL), Idaho Falls, ID (United States)
Aalborg Univ. (Denmark)
Energinet.dk, Fredericia (Denmark)

This paper determines optimum aggregation areas for a given distribution network considering spatial distribution of loads and costs of aggregation. An elitist genetic algorithm combined with a hierarchical clustering and a Thevenin network reduction is implemented to compute strategic locations and aggregate demand within each area. The aggregation reduces large distribution networks having thousands of nodes to an equivalent network with few aggregated loads, thereby significantly reducing the computational burden. Furthermore, it not only helps distribution system operators in making faster operational decisions by understanding during which time of the day will be in need of flexibility, from which specific area, and in which amount, but also enables the flexibilities stemming from small distributed resources to be traded in various power/energy markets. A combination of central and local aggregation scheme where a central aggregator enables market participation, while local aggregators materialize the accepted bids, is implemented to realize this concept. The effectiveness of the proposed method is evaluated by comparing network performances with and without aggregation. Finally, for a given network configuration, steady-state performance of aggregated network is significantly accurate (≈ ±1.5% error) compared to very high errors associated with forecast of individual consumer demand.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Lab. (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Nuclear Energy (NE)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1375244

Report Number(s):: INL/JOU--16-40290; PII: S0142061516325601

Journal Information:: International Journal of Electrical Power and Energy Systems, Journal Name: International Journal of Electrical Power and Energy Systems Vol. 92; ISSN 0142-0615

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (3)

On the Determination of Meshed Distribution Networks Operational Points after Reinforcement Vita, Vasiliki; Lazarou, Stavros; Christodoulou, Christos A. Applied Sciences, Vol. 9, Issue 17 https://doi.org/10.3390/app9173501	journal	August 2019
Calculating Operational Patterns for Electric Vehicle Charging on a Real Distribution Network Based on Renewables’ Production Lazarou, Stavros; Vita, Vasiliki; Christodoulou, Christos Energies, Vol. 11, Issue 9 https://doi.org/10.3390/en11092400	journal	September 2018
Protection Schemes of Meshed Distribution Networks for Smart Grids and Electric Vehicles Lazarou, Stavros; Vita, Vasiliki; Ekonomou, Lambros Energies, Vol. 11, Issue 11 https://doi.org/10.3390/en11113106	journal	November 2018

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